## Line Chart: EGA Performance Across Human-AI Collaboration Models
### Overview
The chart compares Expected Goal Achievement (EGA) scores across five collaboration models (Full Human, Human+AI, AI, AI+Human, AI Only) at two points: 1 and 3 provided human-written plans. All models show distinct performance trajectories, with Full Human maintaining perfect scores.
### Components/Axes
- **X-axis**: "# of provided human-written plans" (values: 1, 3)
- **Y-axis**: "EGA" (scale: 0.0–1.0)
- **Legend**: Right-aligned, color-coded labels:
- Blue Circle: Full Human
- Orange X: Human+AI
- Pink Diamond: AI
- Green Square: AI+Human
- Dark Blue Cross: AI Only
### Detailed Analysis
1. **Full Human (Blue Circle)**
- Constant at 1.0 EGA for both x=1 and x=3
- Perfect performance maintained regardless of plan quantity
2. **Human+AI (Orange X)**
- x=1: ~0.55 EGA
- x=3: ~0.65 EGA
- Steady upward trend with plan quantity
3. **AI (Pink Diamond)**
- x=1: ~0.5 EGA
- x=3: ~0.6 EGA
- Moderate improvement with more plans
4. **AI+Human (Green Square)**
- x=1: ~0.4 EGA
- x=3: ~0.45 EGA
- Smallest absolute gain (+0.05)
5. **AI Only (Dark Blue Cross)**
- x=1: ~0.2 EGA
- x=3: ~0.25 EGA
- Minimal improvement and lowest baseline
### Key Observations
- Full Human performance remains perfectly stable (1.0 EGA)
- Human+AI shows strongest growth (+0.1 EGA) with increased plans
- AI+Human and AI Only demonstrate weakest scalability
- AI Only remains consistently the lowest performer
### Interpretation
The data reveals a clear hierarchy of effectiveness:
1. **Full Human** dominance suggests irreplaceable human contribution
2. **Human+AI** collaboration outperforms pure AI models by 20-30%
3. **AI+Human** underperforms expectations, indicating potential implementation gaps
4. **AI Only**'s poor performance highlights critical limitations in autonomous systems
The trends emphasize that human involvement—whether direct (Full Human) or collaborative (Human+AI)—drives superior outcomes. The AI+Human model's weak gains suggest either suboptimal human-AI integration or insufficient plan quality. Notably, AI's performance improves with more plans, but remains significantly below human-assisted models, indicating fundamental capability gaps in unassisted AI systems.
